Dynamic Clustering Algorithm for Tracking Target with High and Variable Celerity
01 Sep 2016-pp 475-480
TL;DR: This work proposes to build optimal dynamic clusters on the target trajectory to increase energy efficiency and integrates for the first time, to the knowledge, strategies to avoid overlapping clusters and a model to wake up the sensors, adapting to the context of targets with large and variable speed.
Abstract: Target tracking with the wireless sensors networks is to detect and locate a target on its entire path through a region of interest. This application arouses interest in the world of research for its many fields of use. Wireless sensor networks, thanks to their versatility, can be used in many hostile environments and inaccessible to humans. However, with a limited energy, they cannot remain permanently active which can significantly reduce their lifetime. The formation of a cluster network seems an effective mechanism to increase network lifetime ". We propose to build optimal dynamic clusters on the target trajectory. For increasing energy efficiency, our algorithm integrates for the first time, to our knowledge, strategies to avoid overlapping clusters and a model to wake up the sensors, adapting to the context of targets with large and variable speed.
Citations
More filters
30 Sep 2019
TL;DR: It is expected that the results of this study can show the performance of the two evaluation techniques in producing the optimal number of clusters so that grouping information is in accordance with the expected pattern.
Abstract: The clusters number optimization problem is a problem that still requires continuous research so that the information produced can be a consideration. Cluster evaluation techniques with Sum of Square Error (SSE) and Davies Bouldin Index (DBI) are techniques that can evaluate the number of clusters from a data test. Research with these two techniques utilizes Stunting data from a number of regions in Indonesia. The result is information on stunting data which is formed from the optimal number of clusters where the largest SSE is formed at k = 5 and the smallest DBI is formed at k = 5, with values of 23.403 and 1,178 respectively. Changes in the number of clusters also influence the information produced and DBI is proven to produce optimal number of clusters that contain information with a better pattern because it has a small intra-cluster value. It is expected that the results of this study can show the performance of the two evaluation techniques in producing the optimal number of clusters so that grouping information is in accordance with the expected pattern.
9 citations
Cites background from "Dynamic Clustering Algorithm for Tr..."
...DBI juga berhasil menentukan jumlah cluster optimal dalam melacak target dengan celerity variable yang tinggi [7]....
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01 Oct 2017
TL;DR: This paper proposes an energy efficient target tracking algorithm, called triangular cluster-based target tracking (TCTT), which suffers from the boundary problem caused by insufficient switch among clusters when the target moves close to the boundary.
Abstract: Energy efficiency is a critical issue for mobile target tracking in wireless sensor networks, which typically consist of small-sized battery-operated devices with limited processing capability. Clustering techniques are widely adopted for target tracking in large-scale sensor networks to reduce energy consumption and delay. In this paper, we propose an energy efficient target tracking algorithm, called triangular cluster-based target tracking (TCTT). However, clustering based target tracking suffers from the boundary problem caused by insufficient switch among clusters when the target moves close to the boundary. To overcome the boundary problem, we propose a cluster transforming mechanism to prevent loss of the target. To reduce the complexity of accurately locating the target position, we employ the gray model prediction algorithm with wavelet denoising. Through simulation results, we show that our proposed scheme achieves better energy efficiency compared with other typical target tracking algorithms.
2 citations
Additional excerpts
..., static [3][4][5] and dynamic approaches [6][7][8][9]....
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References
More filters
28 Jul 1997
TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
Abstract: The Kalman Filter (KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearizes all nonlinear models so that the traditional linear Kalman filter can be applied. Although the EKF (in its many forms) is a widely used filtering strategy, over thirty years of experience with it has led to a general consensus within the tracking and control community that it is difficult to implement, difficult to tune, and only reliable for systems which are almost linear on the time scale of the update intervals. In this paper a new linear estimator is developed and demonstrated. Using the principle that a set of discretely sampled points can be used to parameterize mean and covariance, the estimator yields performance equivalent to the KF for linear systems yet generalizes elegantly to nonlinear systems without the linearization steps required by the EKF. We show analytically that the expected performance of the new approach is superior to that of the EKF and, in fact, is directly comparable to that of the second order Gauss filter. The method is not restricted to assuming that the distributions of noise sources are Gaussian. We argue that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
5,314 citations
"Dynamic Clustering Algorithm for Tr..." refers methods in this paper
...To evaluate the energy consumed during the monitoring process, we record the energy cost of deferent tasks such as the sensors startup, the time of activity, location and message exchanged with the CH....
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...The Prediction schemes have been proposed in recent years to predict the position of the target which enables to activate only the nodes which are on the trajectory of the target....
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Book•
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01 Jul 1976
TL;DR: This lecture reviews the theory of Markov chains and introduces some of the high quality routines for working with Markov Chains available in QuantEcon.jl.
Abstract: Markov chains are one of the most useful classes of stochastic processes, being • simple, flexible and supported by many elegant theoretical results • valuable for building intuition about random dynamic models • central to quantitative modeling in their own right You will find them in many of the workhorse models of economics and finance. In this lecture we review some of the theory of Markov chains. We will also introduce some of the high quality routines for working with Markov chains available in QuantEcon.jl. Prerequisite knowledge is basic probability and linear algebra.
3,255 citations
"Dynamic Clustering Algorithm for Tr..." refers methods in this paper
...This prediction can be performed using predictive models including : The Kalman filters [11] , or using probabilistic mechanisms such as Markov chains [14]....
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Book•
01 Jan 2000
TL;DR: This chapter discusses Signal Estimation, which automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and changing the values of coefficients in a model to facilitate change detection.
Abstract: INTRODUCTION Extended Summary. Applications. SIGNAL ESTIMATION On--Line Approaches. Off--Line Approaches. PARAMETER ESTIMATION Adaptive Filtering. Change Detection Based on Sliding Windows Change Detection Based on Filter Banks STATE ESTIMATION Kalman Filtering Change Detection Based on Likelihood Ratios Change Detection Based on Multiple Models Change Detection Based on Algebraical Consistency Tests THEORY Evaluation Theory Linear Estimation A. Signal models and notation B. Fault detection terminology
1,170 citations
"Dynamic Clustering Algorithm for Tr..." refers methods in this paper
...The extended Kalman filter [11] combined with detection mechanisms for changes of direction as CuSum [12] can effectively calculate future coordinates of the target and wake up the sensors accordingly....
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TL;DR: The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime.
Abstract: Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani’s rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime.
380 citations
"Dynamic Clustering Algorithm for Tr..." refers background or methods in this paper
...Forming a clustered network seems an effective mechanism to increase network’s lifetime....
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...This test is performed with 1000 nodes distributed randomly, with a low speed of 10 m /s, to have the opportunity to compare our algorithm with CHEW and DKF DC algorithms that support only low speeds....
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20 Mar 2014
TL;DR: Two new clustering-based protocols for heterogeneous WSNs are proposed and evaluated, which are called single-hop energy-efficient clustering protocol (S-EECP) and multi-hopEnergy- efficient clustering Protocol (M-E ECP).
Abstract: Over the last couple of decades, clustering-based protocols are believed to be the best for heterogeneous wireless sensor networks (WSNs) because they work on the principle of divide and conquer. In this study, the authors propose and evaluate two new clustering-based protocols for heterogeneous WSNs, which are called single-hop energy-efficient clustering protocol (S-EECP) and multi-hop energy-efficient clustering protocol (M-EECP). In S-EECP, the cluster heads (CHs) are elected by a weighted probability based on the ratio between residual energy of each node and average energy of the network. The nodes with high initial energy and residual energy will have more chances to be elected as CHs than nodes with low energy whereas in M-EECP, the elected CHs communicate the data packets to the base station via multi-hop communication approach. To analyse the lifetime of the network, the authors assume three types of sensor nodes equipped with different battery energy. Finally, simulation results indicate that the authors protocols prolong network lifetime, and achieve load balance among the CHs better than the existing clustering protocols.
149 citations
"Dynamic Clustering Algorithm for Tr..." refers background or methods in this paper
...This test is performed with 1000 nodes distributed randomly, with a low speed of 10 m /s, to have the opportunity to compare our algorithm with CHEW and DKF DC algorithms that support only low speeds....
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...Many researchers focused on the development of efficient energy clustering algorithms [1-5], however, these algorithms are not suitable for applications specifications related to the targets tracking....
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